Spatial regularization for nonlinear unmixing of hyperspectral data with vector-valued kernel functions
Résumé
This communication introduces a new framework for incorporating spatial regularization into a nonlinear unmixing procedure dedicated to hyperspectral data. The proposed model promotes smooth spatial variations of the nonlinear component in the mixing model. The spatial regularizer and the nonlinear contributions are jointly modeled by a vector-valued function that lies in a reproducing kernel Hilbert space (RKHS). The unmixing problem is strictly convex and reduces to a quadratic programming (QP) problem. Simulations on synthetic data illustrate the effectiveness of the proposed approach.
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